NonParametric Estimation in Survival Models


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1 NonParametric Estimation in Survival Models Germán Rodríguez Spring, 2001; revised Spring 2005 We now discuss the analysis of survival data without parametric assumptions about the form of the distribution. 1 One Sample: KaplanMeier Our first topic is nonparametric estimation of the survival function. If the data were not censored, the obvious estimate would be the empirical survival function Ŝ(t) = 1 n I{t i > t}, n where I is the indicator function that takes the value 1 if the condition in braces is true and 0 otherwise. The estimator is simply the proportion alive at t. 1.1 Estimation with Censored Data Kaplan and Meier (1958) extended the estimate to censored data. Let t (1) < t (2) <... < t (m) denote the distinct ordered times of death (not counting censoring times). Let d i be the number of deaths at t (i), and let n i be the number alive just before t (i). This is the number exposed to risk at time t (i). Then the Kaplan Meier or product limit estimate of the survivor function is Ŝ(t) = i:t (i) <t ( 1 d i n i A heuristic justification of the estimate is as follows. To survive to time t you must first survive to t (1). You must then survive from t (1) to t (2) given ). 1
2 that you have already survived to t (1). And so on. Because there are no deaths between t (i 1) and t (i), we take the probability of dying between these times to be zero. The conditional probability of dying at t (i) given that the subject was alive just before can be estimated by d i /n i. The conditional probability of surviving time t (i) is the complement 1 d i /n i. The overall unconditional probability of surviving to t is obtained by multiplying the conditional probabilities for all relevant times up to t. The KaplanMeier estimate is a step function with discontinuities or jumps at the observed death times. Figure 1 shows KaplanMeier estimates for the treated and control groups in the famous Gehan data (see Cox, 1972 or Andersen et al., 1993, p ) Figure 1: KaplanMeier Estimates for Gehan Data If there is no censoring, the KM estimate coincides with the empirical survival function. If the last observation happens to be a censored case, as is the case in the treated group in the Gehan data, the estimate is undefined beyond the last death. 2
3 1.2 Nonparametric Maximum Likelihood The KM estimator has a nice interpretation as a nonparametric maximum likelihood estimator (NPML). A rigorous treatment of this notion is beyond the scope of the course, but the original article by KM provides a more intuitive approach. We consider the contribution to the likelihood of cases that die or are censored at time t. If a subject is censored at t its contribution to the likelihood is S(t). In order to maximize the likelihood we would like to make this as large as possible. Because a survival function must be nonincreasing, the best we can do is keep it constant at t. In other words, the estimated survival function doesn t change at censoring times. If a subject dies at t then this is one of the distinct times of death that we introduced before. Say it is t (i). We need to make the survival function just before t (i) as large as possible. The largest it can be is the value at the previous time of death or 1, whichever is less. We also need to make the survival at t (i) itself as small as possible. This means we need a discontinuity at t (i). Let c i denote the number of cases censored between t (i) and t (i+1), and let d i be the number of cases that die at t (i). Then the likelihood function takes the form m L = [S(t (i 1) ) S(t (i) )] d i S(t (i) ) c i, where the product is over the m distinct times of death, and we take t (0) = 0 with S(t (0) ) = 1. The problem now is to estimate m parameters representing the values of the survival function at the death times t (1), t (2),..., t (m). Write π i = S(t (i) )/S(t (i 1) ) for the conditional probability of surviving from S(t (i 1) ) to S(t (i) ). Then we can write and the likelihood becomes S(t (i) ) = π 1 π 2... π i, m L = (1 π i ) d i π c i i (π 1π 2... π i 1 ) d i+c i. Note that all cases who die at t (i) or are censored between t (i) and t (i+1) contribute a term π j to each of the previous times of death from t (1) to t (i 1). In addition, those who die at t (i) contribute 1 π i, and the censored 3
4 cases contribute an additional π i. Let n i = j i (d j + c j ) denote the total number exposed to risk at t (i). We can then collect terms on each π i and write the likelihood as m L = (1 π i ) d i π n i d i a binomial likelihood. The m.l.e. of π i is then ˆπ i = n i d i n i i, = 1 d i n i. The KM estimator follows from multiplying these conditional probabilities. 1.3 Greenwood s Formula From the likelihood function obtained above it follows that the large sample variance of ˆπ i conditional on the data n i and d i is given by the usual binomial formula var(ˆπ i ) = π i(1 π i ) n i. Perhaps less obviously, cov(ˆπ i, ˆπ j ) = 0 for i j, so the covariances of the contributions from different times of death are all zero. You can verify this result by taking logs and then first and second derivatives of the loglikelihood function. To obtain the large sample variance of Ŝ(t), the KM estimate of the survival function, we need to apply the delta method twice. First we take logs, so that instead of the variance of a product we can find the variance of a sum, working with i K i = log Ŝ(t (i)) = log ˆπ j. j=1 Now we need to find the variance of the log of ˆπ i. This will be our first application of the delta method. The largesample variance of a function f of a random variable X is var(f(x)) = (f (X)) 2 var(x), so we just multiply the variance of X by the derivative of the transformation. In our case the function is the log and we obtain var(log ˆπ i ) = ( 1ˆπ i ) 2 var( ˆπ i ) = 1 π i n i π i. 4
5 Because K i is a sum and the covariances of the π i s (and hence of the log π i s) are zero, we find var(log Ŝ(t (i))) = i j=1 1 π j n j π j = d j n j (n j d j ). Now we have to use the delta method again, this time to get the variance of the survivor function from the variance of its log: var(ŝ(t (i))) = [Ŝ(t (i))] 2 i j=1 1 ˆπ j n j ˆπ j. This result is known as Greenwood s formula. You may question the derivation because it conditions on the n j which are random variables, but the result is in the spirit of likelihood theory, conditioning on all observed quantities, and has been justified rigourously. Peterson (1977) has shown that the KM estimator Ŝ(t) is consistent, and Breslow and Crowley (1974) show that n(ŝ(t) S(t)) converges in law to a Gaussian process with expectation 0 and a variancecovariance function that may be approximated using Greenwood s formula. For a modern treatment of the estimator from the point of view of counting processes see Andersen et al. (1993). 1.4 The NelsonAalen Estimator Consider estimating the cumulative hazard Λ(t). A simple approach is to start from an estimator of S(t) and take minus the log. An alternative approach is to estimate the cumulative hazard directly using the Nelson Aalen estimator: i d j ˆΛ(t (i) ) =. n j Intuitively, this expression is estimating the hazard at each distinct time of death t (j) as the ratio of the number of deaths to the number exposed. The cumulative hazard up to time t is simply the sum of the hazards at all death times up to t, and has a nice interpretation as the expected number of deaths in (0, t] per unit at risk. This estimator has a strong justification in terms of the theory of counting processes. The variance of ˆΛ(t (i) ) can be approximated by var( log Ŝ(t (i))), which we obtained on our way to Greenwood s formula. Therneau and Grambsch (2000) discuss alternative approximations. 5 j=1
6 Breslow (1972) suggested estimating the survival function as Ŝ(t) = exp{ ˆΛ(t)}, where ˆΛ(t) is the NelsonAalen estimator of the integrated hazard. The Breslow estimator and the KM estimator are asymptotically equivalent, and usually are quite close to each other, particularly when the number of deaths is small relative to the number exposed. 1.5 Expectation of Life If Ŝ(t (m)) = 0 then one can estimate µ = E(T ) as the integral of the KM estimate: m ˆµ = Ŝ(t)dt = (t (i) t (i 1) )Ŝ(t (i)). 0 Can you figure out the variance of ˆµ? 2 ksamples: MantelHaenszel Consider now the problem of comparing two or more survivor functions, for example urban versus rural, or treated versus control. Let t (1) < t (2) <... < t (m) denote the distinct times of death observed in the total sample, obtained by combining all groups of interest. Let d ij = deaths at time t (i) in group j, and n ij = number at risk at time t (i) in group j. We also let d i and n i denote the total number of deaths and subjects at risk at time t (i). If the survival probabilities are the same in all groups, then the d i deaths at time t (i) should be distributed among the k groups in proportion to the number at risk. Thus, conditional on d i and n ij, E(d ij ) = d i n ij n i = n ij d i n i, where the last term shows that we can also view this calculation as applying an overall failure rate d i /n i to the n ij subjects in group j. 6
7 We now proceed beyond the mean to obtain the distribution of these counts. Imagine setting up a contingency table at each distinct failure time, with rows given by survival status and columns given by group membership. The entries in the table are d ij or n ij d ij, the row totals are d i and n i and the column totals are n ij. The distribution of the counts conditional on both the row and column totals is hypergeometric. (We mentioned this distribution briefly in WWS509 when we considered contingency tables with both margins fixed.) The hypergeometric distribution has mean as given above, variance var(d ij ) = d i(n i d i ) n ij (1 n ij ), n i 1 n i n i and covariance cov(d ir, d is ) = d i(n i d i ) n i 1 n ir n is n 2. i Let d i denote the vector of deaths at time t (i), with mean E( d i ) and varcov matrix var( d i ). We sum these over all times to obtain m D = [ d i E( d i )] and m V = var( d i ). Mantel and Haenszel proposed testing the equality of the k survival functions by treating the quadratic form H 0 : S 1 (t) = S 2 (t) =... = S k (t) Q = D V D as a χ 2 statistic with k 1 degrees of freedom. Here V is any generalized inverse of V. Omitting the ith group from the calculation of D and V will do; the test is invariant to the choice of omitted group. For k = 2 we get z = Q = (di1 E(d i1 )). var(di1 ) An approximation for k 2 which does not require matrix inversion treats i j (O ij E ij ) 2 E ij, where O ij denotes observed and E ij expected deaths at time t (i) in group j, as a χ 2 statistic with k 1 d.f. 7
8 The MantelHaenszel test can be derived as a linear rank test, and in that context is often called the logrank or Savage test. Kalbfleisch and Prentice have proposed an extension to censored data of the Wilcoxon test. Other alternatives have been proposed by Gehan (1965) and Breslow (1970), but the MH test is the most popular one. 3 Regression: Cox s Model Let us consider the more general problem where we have a vector x of covariates. The ksample problem can be viewed as the special case where the x s are dummy variables denoting group membership. Recall the basic model λ(t, x) = λ 0 (t)e x β, and consider estimation of β without making any assumptions about the baseline hazard λ 0 (t). 3.1 Cox s Partial Likelihood In his 1972 paper Cox proposed fitting the model by maximizing a special likelihood. Let t (1) < t (2) <... < t (m) denote the observed distinct times of death, as before, and consider what happens at t (i). Let R i denote the risk set at t (i), defined as the set of indices of the subjects that are alive just before t (i). Thus, R 0 = {1, 2,..., n}. Suppose first that there are no ties in the observation times, so one and only person subject failed at t (i). Let s call this subject j(i). What is the conditional probability that this particular subject would fail at t (i) given the risk set R i and the fact that exactly one subject fails at that time? Answer: λ(t (i), x j(i) )dt j R i λ(t (i), x j )dt. We can write this probability in terms of the baseline hazard and relative risk as λ 0 (t (i) )e x j(i) β j R i λ 0 (t (i) )e x j β, 8
9 and we notice that the baseline hazard cancels, so the probability in question is e x j(i) β j R i e x j β and does not depend on the baseline hazard λ 0 (t). Cox proposed multiplying these probabilities together over all distinct failure times and treating the resulting product L = m e x j(i) β j R i e x j β as if it was an ordinary likelihood. In his original paper Cox called this a conditional likelihood because it is a product of conditional probabilities, but later abandoned the name because it is misleading: L is not itself a conditional probability. Kalbfleisch and Prentice considered the case where the covariates are fixed over time and showed that L is the marginal likelihood of the ranks of the observations, obtained by considering just the order in which people die and not the actual times at which they die. In 1975 Cox provided a more general justification of L as part of the full likelihood in fact, a part that happens to contain most of the information about β and therefore proposed calling L a partial likelihood. This justification is valid even with timevarying covariates. A more rigorous justification of the partial likelihood in terms of the theory of counting processes can be found in Andersen et al. (1993). 3.2 The Score and Information The log of Cox s partial likelihood is logl = i {x (j(i) β log j R i e x j β }. Taking derivatives with respect to β we find the score to be log L i β r = x j(i)r j R i e x j β x jr j R i e x j β. The term to the right of the minus sign is just a weighted average of x r over the risk set R i with weights equal to the relative risks e x j β. Thus, we can 9
10 write the score as U r (β) = log L i β r = x j(i)r A ir (β), where A ir (β) is the weighted average of x r over the risk set R i. Taking derivatives again and changing sign we find the observed information to be 2 log L i e x j β x jr x js ( e x j β ) ( e x j β x jr )( e x j β x js ) = β r β s ( e x j β, ) 2 where all sums are over the risk set R i. The right hand side can be written as the difference of two terms. The first term can be interpreted as a weighted average of the crossproduct of x r and x s. The second term is the product of the weighted averages A ir (β) and A is (β). Thus we can write 2 log L i e x j β x jr x js = β r β s e x j β A jr (β)a js (β). You may recognize this expression as the old desk calculator formula for a covariance, leading to the observed information I(β) = 2 log L i β r β s = m C irs (β), where C irs (β) denotes the weighted covariance of x r and x s over the risk set R i with weights equal to the relative risks e x j β for j R i. Calculation of the score and information is thus relatively simple. In matrix notation u(β) = i (x j(i) A i (β)) and I(β) = i C i (β), where A i (β) is the mean and C i (β) is the variancecovariance matrix of x over the risk set R i with weights e x j β for j R i. Notably, the partial loglikelihood is formally identical with the loglikelihood for a conditional logit model. 3.3 The Problem of Ties The development so far has assumed that only one death occurs at each distinct time t (i). In practice we often observe several deaths, say d i, at t (i). This can happen in several ways: 10
11 The data are discrete, so in fact there is a positive probability of failure at t (i). If this is the case one should really use a discrete model. We will discuss possible approaches below. The data are continuous but have been grouped, so d i represents the number of deaths in some interval around t (i). In this case one would probably be better off estimating a separate parameter for each interval, using a complementaryloglog binomial model or a Poisson model corresponding to a piecewise exponential survival model, as discussed in my WWS509 notes. The data are continuous and are not grouped, but there are a few ties resulting perhaps from coarse measurement. In this case we can extend the argument used to build the likelihood. Let D i denote the set of indices of the d i cases who failed at t (i). The probability that the d i cases that actually fail would be those in D i given the risk set R i and the fact that d i of them fail at t (i) is L = j D i e x j β P i j P i e x j β, where the sum in the denominator is over all possible permutations P i or ways of choosing d i indices from the risk set, and the product is over the set of chosen indices, which we call a permutation. For example assume four people are at risk and two die. The deaths could be {1,2}, {1,3}, {1,4}, {2,3}, {2,4}, {3,4}. We calculate the probability of each of these outcomes. Then we divide the probability of the outcome that actually occurred by the sum of the probabilities of all possible outcomes. This likelihood was proposed by Cox in his original paper. The numerator is easy to calculate, and has the form exp{s i β}, where S i = j D i x j is the sum of the x s over the death set D i. The denominator is difficult to calculate because the number of permutations grows very quickly with d i. Peto (and independently Breslow) proposed approximating the denominator by calculating the sum e x j β over the entire risk set R i and raising it to d i. This leads to the much simpler expression m L e S i β j β j R i e x d i. The PetoBreslow approximation is reasonably good when d i is small relative to n i, and is popular because of its simplicity. Efron proposed a better 11
12 approximation that requires only modest additional computational effort. Consider again our example where two out of four subjects fail. Suppose the subjects that fail are 1 and 2, and let r j = e x j β denote the relative risk for the jth subject. In continuous time one must have failed before the other, we just don t know which. The contributions to the partial likelihood would be r 1 r 1 + r 2 + r 3 + r 4 r 2 r 2 + r 3 + r 4 if 1 failed before 2, or r 2 r 1 r 1 + r 2 + r 3 + r 4 r 1 + r 3 + r 4 if 2 was the first to fail. In both cases the numerator is r 1 r 2. To compute the denominator Peto and Breslow add the risks over the complete risk set both times, using (r 1 + r 2 + r 3 + r 4 ) 2, which is obviously conservative. Efron uses the average risk of subjects 1 and 2 for the second term, so he takes the denominator to be (r 1 + r 2 + r 3 + r 4 )(0.5r r 2 + r 3 + r 4 ). This approximation is much more accurate unless d i is very large relative to n i. For more details see Therneau and Grambsch (2000, Section 3.3). 3.4 Tests of Hypotheses As usual, we have three approaches to testing hypotheses about ˆβ: Likelihood Ratio Test: given two nested models, we treat twice the difference in partial loglikelihoods as a χ 2 statistic with degrees of freedom equal to the difference in the number of parameters. Wald Test: we use the fact that approximately in large samples ˆβ has a multivariate normal distribution with mean β and variancecovariance matrix var( ˆβ) = I 1 (β). Thus, under H 0 : β = β 0, the quadratic form ( ˆβ β 0 ) var 1 ( ˆβ)( ˆβ β 0 ) χ 2 p, where p is the dimension of β. This test is often used for a subset of β. Score Test: we use the fact that approximately in large samples the score u(β) has a multivariate normal distribution with mean 0 and variancecovariance matrix equal to the information matrix. Thus, under H 0 : β = β 0, the quadratic form u(β 0 ) I 1 (β 0 )u(β 0 ) χ 2 p. Note that this test does not require calculating the m.l.e. ˆβ. 12
13 One reason for bringing up the score test is that in the ksample case the score test of H 0 : β = 0 based on Cox s model happens to be the same as the MantelHaenszel logrank test. Here is an outline of the proof. Assume no ties. If β = 0 then the weights used to calculate A i and C i are all 1, A ir happens to be the proportion of the risk set R i that comes from the rth sample (which is the same as the expected number of deaths in that group) and C i is a binomial variancecovariance matrix. If there are ties, Cox s approach leads to the test discussed in Section 2. Use of Peto s approximation is equivalent to omitting the factor (n i d i )/(n i 1) from the variancecovariance matrix. All three tests are asymptotically equivalent. The quality of the normal approximations depends on the sample size, the distribution of the cases over the covariate space, and the extent of censoring. 3.5 TimeVarying Covariates A nice feature of the Cox model and partial likelihood is that it extends easily to the case of timevarying covariates. Note that the partial likelihood is built by considering only what happens at each failure time, so we only need to know the values of the covariates at the distinct times of death. One use of timevarying covariates is to check the assumption of proportionality of hazards. In his original paper Cox analyzes a twosample problem and introduces an auxiliary covariate { 0, in group 0 z i = t c, in group 1 where c is an arbitrary constant close to the mean of t, chosen to avoid numerical instability. If the coefficient of z is 0, the assumption of proportionality of hazards is adequate. A positive value indicates that the ratio of hazards for group 1 over group 0 increases over time. A negative coefficient suggests a declining hazard ratio, a common occurrence. Another use of timevarying covariates is to represent variables that simply change over time. In a study of contraceptive failure, for example, one may treat frequency of intercourse as a timevarying covariate. Another example of a timevarying covariate is education in an analysis of age at marriage. Note that x(t) may represent the actual value of a variable at time t, or any index based on the individual s history up to time t. In a study of the effects of breastfeeding on postpartum amenorrhea, for example, x(t) could represent the total number of suckling episodes in the week preceding t. 13
14 The partial likelihood function with timevarying covariates does not have an interpretation as a marginal likelihood of the ranks. For further details see Cox and Oakes (1984, Chapter 8). 3.6 Estimating the Baseline Survival Interest so far has focused on the regression coefficients β. We now consider how to estimate the baseline hazard λ 0 (t), which dropped out of the partial likelihood. Kalbfleisch and Prentice (1980, Section 4.3) use an argument similar to the derivation of the KaplanMeier estimate, noting that the hazard should assign mass only to the discrete times of death. Let π i denote the conditional survival probability at time t (i) for a baseline subject. To obtain the conditional probability for a subject with covariates x we would need to raise π i to e x β. This leads to a likelihood of the form L = m j D i (1 π i ) e x j β π e x j β i. j R i D i Meier suggested maximizing this likelihood with respect to both the π i and β. A simpler approach is to plugin the estimate ˆβ from the partial likelihood, and maximize the resulting expression with respect to the π i only. If there are no ties, this gives ˆπ i = 1 ˆβ e x ˆβ j(i) ex j(i) j R i e x ˆβ j Think of this as follows. With no covariates, our estimate would be ˆπ i = 1 d i /n i with d i = 1, which is the KM estimate. With covariates we do the same thing, except that we weight each case by its relative risk. The resulting survival probability is then raised to e x j(i) β to turn it into a baseline probability. If there are ties one has to solve. j D i x e ˆβ j 1 π ex j ˆβ i iteratively. A suitable starting value is = e x ˆβ j j R i d i log π i = j R i e x ˆβ. j 14
15 The estimate of the baseline survival function is then a step function Ŝ 0 (t) = i:t (i) <t Cox and Oakes (1984, Section 7.8) describe a simpler estimator that extends the NelsonAalen estimate of the cumulative hazard to the case of covariates. The estimator can be described somewhat heuristically as follows. Treat the baseline hazard as 0 except at failure times. The expected number of deaths at t (i) can be obtained by summing the hazards over the risk set: E(d i ) = λ 0 (t (i) )e x j β. j R i Equating the observed and expected number of deaths at t (i) leads us to estimate λ i = λ 0 (t (i) ) as ˆλ i = d i e x j β, where the sum is over the risk set R i. The cumulative hazard and survival functions are then estimated as ˆΛ 0 (t) = i:t (i) <t ˆπ i. ˆλ i, and Ŝ 0 (t) = e ˆΛ 0 (t). If there are no covariates these reduce to the ordinary NelsonAalen and Breslow estimators described earlier. Having obtained estimates of the baseline hazard and survival, we can obtain fitted hazards and survival functions for any value of x. This task is pretty straightforward when we have timefixed covariates, as all we need to do is multiply the baseline hazard by the relative risk, or raise the baseline survival to the relative risk. With timevarying covariates things get somewhat more complicated, as we have to pick up the appropriate hazard for each distinct failure time depending on the values of the covariates at that point. 3.7 Martingale Residuals Residuals play an important role in checking linear and generalized linear models. Not surprisingly, the concept has been extended to survival models. A lot of this work relies heavily on the terminology and notation of counting processes. We will try to convey the essential ideas in a nontechnical way. 15
16 Instead of focusing on the ith individual s survival time T i, we will introduce a function N i (t) that counts events over time. In survival models N i (t) will be zero while the subject is alive and then it will become one. (In more general eventhistory models N i (t) counts the number of occurrences of the event to subject i by time t.) While survival time T i is a random variable, N i (t) is a stochastic process, a function of time. We will also introduce a function to track the ith individual s exposure. Y i (t) will be one while the individual is in the risk set and zero afterwards. Note that Y i (t) can become zero due to death or due to censoring. To complete the model we add a hazard function λ i (t) representing the ith individuals risk at time t. In a Cox model λ i (t) = λ 0 (t) exp{x i β}. In the terminology of counting processes, the process N i (t) is said to have intensity Y i (t)λ i (t). The intensity is just the risk if the subject is exposed, and is zero otherwise. The probability that N i (t) will jump in a small interval [t, t + dt) conditional on the entire history of the process is given by λ i (t)y i (t)dt, and is proportional to the intensity of the process and the width of the interval. A key feature of this formulation is that t M i (t) = N i (t) λ i (t)y i (t)dt 0 is a martingale, a fact that can be used to establish the properties of tests and estimators using martingale central limit theory. A martingale is essentially a stochastic process without drift. Given two times 0 < t 1 < t 2 the expectation E(M(t 2 )) given the history up to time t 1 is simply M(t 1 ). In other words martingale increments have mean zero. Also, martingale increments are uncorrelated, although not necessarily independent. The integral following the minus sign in the above equation is called the compensator of N i (t). You may think of it as the conditional expected value of the counting process at time t. Subtracting the compensator turns the counting process into a martingale. This equation suggest immediately the first type of residual one can use in Cox models, the socalled Martingale Residual: t ˆM i (t) = N i (t) Y i (t)e ˆβdˆΛ x i 0 (t) 0 where ˆΛ 0 (t) denotes the NelsonAalen estimator of the baseline hazard. Because this is a discrete function with jumps at the observed failure times, the integral in the above equation should be interpreted as a sum over all j such that t (j) < t. Usually the residual is computed at t = (or the largest 16
17 observed survival time), in which case the martingale residual is ˆM i = d i e x i ˆβ ˆΛ0 (t i ), where d i is the usual death indicator and t i is the observation time (to death or censoring) for the ith individual. One possible use of residuals is to find outliers, in this case individuals who lived unusually short or long times, after taking into account the relative risks associated with their observed characteristics. Martingale residuals are just one of several types of residuals that have been proposed for survival models. Others include deviance, score and Schoenfeld residuals. For more details on counting processes, martingales and residuals see Therneau and Grambsch (2000), especially Section 2.2 and Chapter Models for Discrete and Grouped Data We close with a brief review of alternative approaches for discrete and continuous grouped data, expanding slightly on the WWS509 discussion. Cox (1972) proposed an alternative version of the proportional hazards model for discrete data. In this case the hazard is the conditional probability of dying at time t given survival up to that point, so that λ(t) = Pr{T = t T t}. Cox s discrete logistic model assumes that the conditional odds of surviving t (i) are proportional to some baseline odds, so that λ(t, x) 1 λ(t, x) = λ 0(t) 1 λ 0 (t) ex β. Note that taking logs on both sides results in a logit model, where the logit of the discrete hazard is linear on β. Do not confuse this model with the proportional odds model, where the unconditional odds of survival (or odds of dying) are proportional for different values of x. If the λ 0 (t) are small so that 1 λ 0 (t) is close to one, this model will be similar to the proportional hazards model, as the odds are close to the hazard itself. A nice property of this model is that the partial likelihood turns out to be identical to that of the continuoustime model. To see this point note 17
18 that under this model the hazard at time t i for an individual with covariate values x is λ(t i, x) = θ ie x β 1 + θ i e x β, where θ i = λ 0 (t i )/(1 λ 0 (t i )) denotes the baseline conditional odds of surviving t i. Suppose there are two cases exposed at time t i, labelled 1 and 2. The probability that 1 dies and 2 does not is λ(t i, x 1 )(1 λ(t i, x 2 )) = θ ie x 1 β 1 + θ i e x 1 β θ i e x 2 β. The probability that 2 dies and 1 does not has a similar structure, with θ i e x 2 β in the numerator and the same denominator. When we divide the probability of one of these outcomes by the sum of the two, the denominators cancel out, as do the θ i. Thus, the conditional probability is e x j(i) β j R i e x j β, which is exactly the same as in the continuous case. (You may wonder why we did not consider the 1 λ s in the continuous case. It turns out we didn t have to. In you repeat the continuoustime derivation including terms of the form λdt for deaths and 1 λdt for survivors you will discover that the dt s for deaths cancel out but those for survivors do not, and as dt 0 the terms 1 λdt 1 and drop from the likelihood.) There is an alternative approach to discrete data that is particularly appropriate when you have grouped continuous times. See Kalbfleisch and Prentice (1980), Section and Section Suppose we wanted a discrete time model that preserves the relationship between survivor functions in the continuous time model, namely S(t, x) = S 0 (t) ex β. In the discrete case we have S(t, x) = u<t (1 λ(u, x)), so we must have Solving for the hazard we get 1 λ(t i, x) = (1 λ 0 (t i )) ex β. λ(t i, x) = 1 (1 λ 0 (t i )) ex β. 18
19 The linearizing transformation for this model is the complementary loglog link, log( log(1 λ)). To see why this model is uniquely appropriate for grouped data suppose we only observe failures in intervals [0 = τ 0, τ 1 ), [τ 1, τ 2 ),..., [τ k 1, τ k = ). Suppose the hazard is continuous and satisfies a standard proportional hazards model. The probability of surviving interval i for a subject with covariates x is Pr{T > τ i T > τ i 1, x} = S(τ i, x) S(τ i 1, x). Writing this in terms of the baseline survival we get ( ) S0 (τ i ) e x β = (e τ ) i e λ τ 0 (t)dt x β i 1. S 0 (τ i 1 ) In view of this result, we define the baseline hazard in interval (τ i 1, τ i ) as λ 0i = 1 e τ i τ i 1 λ 0 (t)dt. The hazard for an individual with covariates x in the same interval then becomes λ i (x) = 1 (1 λ 0i ) ex β, and can be linearized using the cloglog transformation. This is the only discrete model appropriate for grouped data from a continuoustime proportional hazards model. Unfortunately, it cannot be estimated nonparametrically using a partial likelihood argument. (If you try to construct a partial likelihood you will discover that the λ 0i s do not drop out of the likelihood.) In practice, both the logit and the complementary loglog discrete models can be estimated via ordinary likelihood techniques by treating the baseline hazard at each discrete failure time (or interval) as a separate parameter to be estimated, provided of course one has enough failures at each time (or interval). The resulting likelihood is the same as that of a binomial model with logit or cloglog link, so either model can be easily fitted with standard software. One difficulty with discrete models generated by grouping continuous data has to do with censored cases that may have been exposed part of the interval. The only way to handle these cases is to censor them at the 19
20 beginning of the interval, which throws away some information. This is not a problem with piecewise exponential models based on the Poisson equivalence, because one can easily take into account partial exposure in the offset. 20
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